Posts Tagged ‘machine learning’
IEOR alum applies machine learning to personalized healthcare
Yonatan Mintz, a 2018 Berkeley IEOR Ph.D. graduate, was recently appointed Assistant Professor at the University of Wisconsin-Madison’s Department of Industrial and Systems Engineering. Mintz applies optimization and machine learning methods to tailor healthcare interventions to individuals. Mintz’s research portfolio includes leveraging patient data to hone personalized health and wellness solutions through wearable technology, to…
Read MoreIEOR undergraduates selected as finalists for INFORMS Operations Research Prize
Three former Berkeley IEOR/ORMS undergraduate students, Liangyuan Na (B.A. Operations Research & Management Science ’18), Cong Yang (B.S. Industrial Engineering & Operations Research ’18), and Chi-Cheng Lo (B.S. Industrial Engineering & Operations Research ’18) are finalists in the INFORMS Undergraduate Operations Research Prize Competition. The students were advised by IEOR assistant professor Anil Aswani, and…
Read MoreMathieu Laurière — Machine Learning Methods for Mean Field Control and Mean Field Games
Abstract: Mean field games (MFG) and mean field control (MFC) describe the behavior of agents interacting in a symmetric fashion when the number of agents grows to infinity. The first theory captures a notion of Nash equilibrium for selfish players while the second one focuses on the notion of social cost for cooperative agents. In…
Read MoreMathieu Laurière — Machine Learning Methods for Mean Field Control and Mean Field Games
Abstract: Mean field games (MFG) and mean field control (MFC) describe the behavior of agents interacting in a symmetric fashion when the number of agents grows to infinity. The first theory captures a notion of Nash equilibrium for selfish players while the second one focuses on the notion of social cost for cooperative agents. In…
Read MoreRichard Y. Zhang — Scalable and Guaranteed Computation: Optimization and Machine Learning for the Future Electric Grid
Abstract: Computation promises to greatly enhance the electric grid through optimization and machine learning. However, many computational problems remain unsolved at the scale, speed, and quality necessary for the real world, due to issues of complexity and nonconvexity. In the first part of this talk, we solve the optimization problem known as optimal power flow…
Read MoreRichard Y. Zhang — Scalable and Guaranteed Computation: Optimization and Machine Learning for the Future Electric Grid
Abstract: Computation promises to greatly enhance the electric grid through optimization and machine learning. However, many computational problems remain unsolved at the scale, speed, and quality necessary for the real world, due to issues of complexity and nonconvexity. In the first part of this talk, we solve the optimization problem known as optimal power flow…
Read MoreCheng-Ju Wu — Machine Learning for Detection and Diagnosis of Disease
Abstract: The presentation will cover our recent developments in machine learning’s application to lung cancer detection and diabetes diagnosis. Lung cancer is the leading cause of cancer deaths world-wide. Early detection of cancer is critical for therapeutic effectiveness and survival improvement. DNA methylation is known to provide potential biomarkers for assessment of cancer risk. Early…
Read MoreCheng-Ju Wu — Machine Learning for Detection and Diagnosis of Disease
Abstract: The presentation will cover our recent developments in machine learning’s application to lung cancer detection and diabetes diagnosis. Lung cancer is the leading cause of cancer deaths world-wide. Early detection of cancer is critical for therapeutic effectiveness and survival improvement. DNA methylation is known to provide potential biomarkers for assessment of cancer risk. Early…
Read MoreNew machine learning technique may help prevent blindness for millions of people with diabetes
Diabetic retinopathy (DR) is the most common cause of vision loss among people with diabetes and a leading cause of blindness. Right now, there are an estimated 415 million people suffering from diabetes worldwide, and this number is projected to grow to 642 million by 2040. Approximately one-third of diabetic patients have DR. While blindness…
Read MoreGrigas to Investigate New Framework For Operations-Driven Machine Learning
IEOR professor Paul Grigas has just been awarded $290,060 by the National Science Foundation to improve operational decision-making by leveraging data and machine learning. Grigas will collaborate with Adam Elamchtoub from Columbia University to advance a new statistical learning framework called Smart “Predict, then Optimize” (SPO) which aims to improve optimization and prediction for better decisions in sectors such as transportation,…
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